code: def test_bernoulli_entropy():
      """distribution test"""
      logits = torch.from_numpy(np.random.randn(5))
      dist = BernoulliProbabilityDistribution(
          logits=logits, action_space=spaces.MultiBinary(5)
      )
      assert dist.entropy().numpy().ndim == 0
  
      logits = torch.from_numpy(np.random.randn(100, 5))
      dist = BernoulliProbabilityDistribution(
          logits=logits, action_space=spaces.MultiBinary(5)
      )
      assert dist.entropy().numpy().ndim == 1
      assert dist.entropy().numpy().shape == (100,)
  
      logits = torch.from_numpy(np.random.randn(100, 8, 5))
      dist = BernoulliProbabilityDistribution(
          logits=logits, action_space=spaces.MultiBinary(5)
      )
      assert dist.entropy().numpy().ndim == 2
      assert dist.entropy().numpy().shape == (100, 8)
  
file_path: maze/maze/test/distributions/test_bernoulli.py
imports: import numpy
  import torch
  from gym import spaces
  from maze.distributions.bernoulli import BernoulliProbabilityDistribution
label_desc: Use image node_id 3 for calling a global function with example usage: test_bernoulli_entropy() without return types